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get_top_250_movies

Retrieve IMDb's top 250 movies in paginated format by specifying a starting index to get the next 5 ranked films.

Instructions

Get the top 250 movies from IMDb with pagination. Args: start: The starting index (0-based) to retrieve movies from. Returns: JSON object containing 5 top movies starting from the specified index.

Input Schema

NameRequiredDescriptionDefault
startYes

Input Schema (JSON Schema)

{ "properties": { "start": { "title": "Start", "type": "integer" } }, "required": [ "start" ], "type": "object" }

Implementation Reference

  • The primary handler function for the 'get_top_250_movies' tool. It is decorated with @mcp.tool() which handles both registration and schema definition via the docstring. Implements the core logic: fetches top 250 movies from the IMDb API endpoint and returns a paginated JSON response using helper functions.
    @mcp.tool() async def get_top_250_movies(start: int, ctx: Context) -> str: """Get the top 250 movies from IMDb with pagination. Args: start: The starting index (0-based) to retrieve movies from. Returns: JSON object containing 5 top movies starting from the specified index. """ top_250_url = f"{BASE_URL}/top250-movies" top_250_data = await make_imdb_request(top_250_url, {}, ctx) if not top_250_data: return "Unable to fetch top 250 movies data." return json.dumps(paginated_response(top_250_data, start, len(top_250_data)), indent=4)
  • Helper function make_imdb_request used by get_top_250_movies to perform HTTP requests to the IMDb API with caching, error handling, and API key management.
    async def make_imdb_request(url: str, querystring: dict[str, Any], ctx: Optional[Context] = None) -> Optional[Dict[str, Any]]: """Make a request to the IMDb API with proper error handling and caching.""" # Check if it's time to clean the cache cache_manager.cleanup_if_needed() # Create a cache key from the URL and querystring cache_key = f"{url}_{str(querystring)}" # Try to get from cache first cached_data = cache_manager.cache.get(cache_key) if cached_data: return cached_data # Get API key from session config or fallback to environment variable api_key = None if ctx and hasattr(ctx, 'session_config') and ctx.session_config: api_key = ctx.session_config.rapidApiKeyImdb if not api_key: api_key = os.getenv("RAPID_API_KEY_IMDB") # Not in cache, make the request headers = { "x-rapidapi-key": api_key, "x-rapidapi-host": "imdb236.p.rapidapi.com", } if not api_key: raise ValueError("API key not found. Please set the RAPID_API_KEY_IMDB environment variable or provide rapidApiKeyImdb in the request.") try: response = requests.get(url, headers=headers, params=querystring, timeout=30.0) response.raise_for_status() data = response.json() # Cache the response cache_manager.cache.set(cache_key, data) return data except Exception as e: raise ValueError(f"Unable to fetch data from IMDb. Please try again later. Error: {e}")
  • Helper function paginated_response used by get_top_250_movies to format the response with pagination (page size 5).
    def paginated_response(items, start, total_count=None): """Format a paginated response with a fixed page size of 5.""" if total_count is None: total_count = len(items) # Validate starting index start = max(0, min(total_count - 1 if total_count > 0 else 0, start)) # Fixed page size of 5 page_size = 5 end = min(start + page_size, total_count) return { "items": items[start:end], "start": start, "count": end - start, "totalCount": total_count, "hasMore": end < total_count, "nextStart": end if end < total_count else None }
  • Call to register_tools(server) which defines and registers the get_top_250_movies tool (and others) with the FastMCP server instance.
    register_tools(server)
  • Alternative call to register_tools(server) in stdio transport mode, registering the get_top_250_movies tool.
    register_tools(server)

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